

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Any      		=					tempfile.mkdtemp()

														A      :					Optional[Any]      		=					[
														    '''[UNK]''',
														    '''[CLS]''',
														    '''[SEP]''',
														    '''[PAD]''',
														    '''[MASK]''',
														    '''want''',
														    '''##want''',
														    '''##ed''',
														    '''wa''',
														    '''un''',
														    '''runn''',
														    '''##ing''',
														    ''',''',
														    '''low''',
														    '''lowest''',
														]
														A      :					Any      		=					os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['''vocab_file''']						)
														with open(self.vocab_file    ,						'''w'''    ,						encoding='''utf-8'''						) as vocab_writer:
																					vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]						)						)

														A      :					Optional[int]      		=					{
														    '''do_resize''': True,
														    '''size''': 20,
														    '''do_center_crop''': True,
														    '''crop_size''': 18,
														    '''do_normalize''': True,
														    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
														    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
														}
														A      :					List[str]      		=					os.path.join(self.tmpdirname    ,						__UpperCAmelCase						)
														with open(self.image_processor_file    ,						'''w'''    ,						encoding='''utf-8'''						) as fp:
																					json.dump(__UpperCAmelCase    ,						__UpperCAmelCase						)




							def       snake_case			(					self    ,						**__UpperCAmelCase						)		->       List[str]:
														return BertTokenizer.from_pretrained(self.tmpdirname    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						**__UpperCAmelCase						)		->       Union[str, Any]:
														return BertTokenizerFast.from_pretrained(self.tmpdirname    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						**__UpperCAmelCase						)		->       List[Any]:
														return EfficientNetImageProcessor.from_pretrained(self.tmpdirname    ,						**__UpperCAmelCase						)




							def       snake_case			(					self						)		->       int:
														shutil.rmtree(self.tmpdirname						)




							def       snake_case			(					self						)		->       str:
														A      :					Dict      		=					[np.random.randint(2_55    ,						size=(3, 30, 4_00)    ,						dtype=np.uinta						)]
														A      :					Union[str, Any]      		=					[Image.fromarray(np.moveaxis(__UpperCAmelCase    ,						0    ,						-1						)						) for x in image_inputs]
														return image_inputs




							def       snake_case			(					self						)		->       Tuple:
														A      :					Any      		=					self.get_tokenizer()
														A      :					List[str]      		=					self.get_rust_tokenizer()
														A      :					Any      		=					self.get_image_processor()

														A      :					List[Any]      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)
														processor_slow.save_pretrained(self.tmpdirname						)
														A      :					Optional[Any]      		=					AlignProcessor.from_pretrained(self.tmpdirname    ,						use_fast=__UpperCAmelCase						)

														A      :					int      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)
														processor_fast.save_pretrained(self.tmpdirname						)
														A      :					Dict      		=					AlignProcessor.from_pretrained(self.tmpdirname						)

														self.assertEqual(processor_slow.tokenizer.get_vocab()    ,						tokenizer_slow.get_vocab()						)
														self.assertEqual(processor_fast.tokenizer.get_vocab()    ,						tokenizer_fast.get_vocab()						)
														self.assertEqual(tokenizer_slow.get_vocab()    ,						tokenizer_fast.get_vocab()						)
														self.assertIsInstance(processor_slow.tokenizer    ,						__UpperCAmelCase						)
														self.assertIsInstance(processor_fast.tokenizer    ,						__UpperCAmelCase						)

														self.assertEqual(processor_slow.image_processor.to_json_string()    ,						image_processor.to_json_string()						)
														self.assertEqual(processor_fast.image_processor.to_json_string()    ,						image_processor.to_json_string()						)
														self.assertIsInstance(processor_slow.image_processor    ,						__UpperCAmelCase						)
														self.assertIsInstance(processor_fast.image_processor    ,						__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														A      :					Tuple      		=					AlignProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()						)
														processor.save_pretrained(self.tmpdirname						)

														A      :					Optional[int]      		=					self.get_tokenizer(bos_token='''(BOS)'''    ,						eos_token='''(EOS)'''						)
														A      :					int      		=					self.get_image_processor(do_normalize=__UpperCAmelCase    ,						padding_value=1.0						)

														A      :					Optional[int]      		=					AlignProcessor.from_pretrained(
														    self.tmpdirname    ,						bos_token='''(BOS)'''    ,						eos_token='''(EOS)'''    ,						do_normalize=__UpperCAmelCase    ,						padding_value=1.0						)

														self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab()						)
														self.assertIsInstance(processor.tokenizer    ,						__UpperCAmelCase						)

														self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor_add_kwargs.to_json_string()						)
														self.assertIsInstance(processor.image_processor    ,						__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														A      :					List[Any]      		=					self.get_image_processor()
														A      :					Union[str, Any]      		=					self.get_tokenizer()

														A      :					Optional[int]      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)

														A      :					Optional[int]      		=					self.prepare_image_inputs()

														A      :					Union[str, Any]      		=					image_processor(__UpperCAmelCase    ,						return_tensors='''np'''						)
														A      :					Optional[Any]      		=					processor(images=__UpperCAmelCase    ,						return_tensors='''np'''						)

														for key in input_image_proc.keys():
																					self.assertAlmostEqual(input_image_proc[key].sum()    ,						input_processor[key].sum()    ,						delta=1E-2						)




							def       snake_case			(					self						)		->       str:
														A      :					Any      		=					self.get_image_processor()
														A      :					Tuple      		=					self.get_tokenizer()

														A      :					int      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)

														A      :					Tuple      		=					'''lower newer'''

														A      :					int      		=					processor(text=__UpperCAmelCase						)

														A      :					Dict      		=					tokenizer(__UpperCAmelCase    ,						padding='''max_length'''    ,						max_length=64						)

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key]						)




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Dict      		=					self.get_image_processor()
														A      :					List[str]      		=					self.get_tokenizer()

														A      :					Tuple      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)

														A      :					List[Any]      		=					'''lower newer'''
														A      :					Optional[Any]      		=					self.prepare_image_inputs()

														A      :					Tuple      		=					processor(text=__UpperCAmelCase    ,						images=__UpperCAmelCase						)

														self.assertListEqual(list(inputs.keys()						)    ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']						)

														# test if it raises when no input is passed
														with pytest.raises(__UpperCAmelCase						):
																					processor()




							def       snake_case			(					self						)		->       Tuple:
														A      :					Any      		=					self.get_image_processor()
														A      :					Optional[int]      		=					self.get_tokenizer()

														A      :					Tuple      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														A      :					Any      		=					processor.batch_decode(__UpperCAmelCase						)
														A      :					str      		=					tokenizer.batch_decode(__UpperCAmelCase						)

														self.assertListEqual(__UpperCAmelCase    ,						__UpperCAmelCase						)






							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					str      		=					self.get_image_processor()
														A      :					Optional[Any]      		=					self.get_tokenizer()

														A      :					List[Any]      		=					AlignProcessor(tokenizer=__UpperCAmelCase    ,						image_processor=__UpperCAmelCase						)

														A      :					Tuple      		=					'''lower newer'''
														A      :					Tuple      		=					self.prepare_image_inputs()

														A      :					List[Any]      		=					processor(text=__UpperCAmelCase    ,						images=__UpperCAmelCase						)

														self.assertListEqual(list(inputs.keys()						)    ,						processor.model_input_names						)









def        snake_case__		(    lowerCamelCase_ = 1000		):
							return sum(e for e in range(3     ,				lowerCamelCase_		) if e % 3 == 0 or e % 5 == 0		)


if __name__ == "__main__":
		print(F"{solution() = }")






